Results 1 - 10 of 2,871

Sparse Reconstruction by Separable Approximation

by Stephen J. Wright , Robert D. Nowak , Mário A. T. Figueiredo , 2007
"... Finding sparse approximate solutions to large underdetermined linear systems of equations is a common problem in signal/image processing and statistics. Basis pursuit, the least absolute shrinkage and selection operator (LASSO), wavelet-based deconvolution and reconstruction, and compressed sensing ..."
Abstract - Cited by 373 (38 self) - Add to MetaCart
Finding sparse approximate solutions to large underdetermined linear systems of equations is a common problem in signal/image processing and statistics. Basis pursuit, the least absolute shrinkage and selection operator (LASSO), wavelet-based deconvolution and reconstruction, and compressed sensing

Gradient projection for sparse reconstruction: Application to compressed sensing and other inverse problems

by Mário A. T. Figueiredo, Robert D. Nowak, Stephen J. Wright - IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING , 2007
"... Many problems in signal processing and statistical inference involve finding sparse solutions to under-determined, or ill-conditioned, linear systems of equations. A standard approach consists in minimizing an objective function which includes a quadratic (squared ℓ2) error term combined with a spa ..."
Abstract - Cited by 539 (17 self) - Add to MetaCart
Many problems in signal processing and statistical inference involve finding sparse solutions to under-determined, or ill-conditioned, linear systems of equations. A standard approach consists in minimizing an objective function which includes a quadratic (squared ℓ2) error term combined with a

On sparse reconstruction from Fourier and Gaussian measurements

by Mark Rudelson, Roman Vershynin - Communications on Pure and Applied Mathematics , 2006
"... Abstract. This paper improves upon best known guarantees for exact reconstruction of a sparse signal f from a small universal sample of Fourier measurements. The method for reconstruction that has recently gained momentum in the Sparse Approximation Theory is to relax this highly non-convex problem ..."
Abstract - Cited by 262 (8 self) - Add to MetaCart
Abstract. This paper improves upon best known guarantees for exact reconstruction of a sparse signal f from a small universal sample of Fourier measurements. The method for reconstruction that has recently gained momentum in the Sparse Approximation Theory is to relax this highly non-convex problem

A Note on Sparse Reconstruction Methods

by Martin Burger
"... Abstract. In this paper we discuss some aspects of sparse reconstruction techniques for inverse problems, which recently became popular due to several superior properties compared to linear reconstructions. We briefly review the standard sparse reconstructions based on ℓ 1-minimization of coefficien ..."
Abstract - Cited by 1 (1 self) - Add to MetaCart
Abstract. In this paper we discuss some aspects of sparse reconstruction techniques for inverse problems, which recently became popular due to several superior properties compared to linear reconstructions. We briefly review the standard sparse reconstructions based on ℓ 1-minimization

Bregmanized Nonlocal Regularization for Deconvolution and Sparse Reconstruction

by Xiaoqun Zhang, Martin Burger, Xavier Bresson, Stanley Osher , 2009
"... We propose two algorithms based on Bregman iteration and operator splitting technique for nonlocal TV regularization problems. The convergence of the algorithms is analyzed and applications to deconvolution and sparse reconstruction are presented. ..."
Abstract - Cited by 88 (8 self) - Add to MetaCart
We propose two algorithms based on Bregman iteration and operator splitting technique for nonlocal TV regularization problems. The convergence of the algorithms is analyzed and applications to deconvolution and sparse reconstruction are presented.

DISTRIBUTED ALGORITHMS FOR SPARSE RECONSTRUCTION

by João Mota
"... Many applications require the knowledge of a sparse linear combination of elementary signals that can explain a given signal. This problem is known as “sparse approximation problem ” and arises in many fields of electrical engineering and applied mathematics. The great difficulty when dealing with s ..."
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Many applications require the knowledge of a sparse linear combination of elementary signals that can explain a given signal. This problem is known as “sparse approximation problem ” and arises in many fields of electrical engineering and applied mathematics. The great difficulty when dealing

Sparse reconstruction cost for abnormal event detection

by Yang Cong, Junsong Yuan, Ji Liu - In IEEE Conference on Computer Vision and Pattern Recognition (CVPR , 2011
"... We propose to detect abnormal events via a sparse reconstruction over the normal bases. Given an over-complete normal basis set (e.g., an image sequence or a collection of local spatio-temporal patches), we introduce the sparse reconstruction cost (SRC) over the normal dictionary to measure the norm ..."
Abstract - Cited by 29 (3 self) - Add to MetaCart
We propose to detect abnormal events via a sparse reconstruction over the normal bases. Given an over-complete normal basis set (e.g., an image sequence or a collection of local spatio-temporal patches), we introduce the sparse reconstruction cost (SRC) over the normal dictionary to measure

Sparse reconstruction by convex relaxation: Fourier and Gaussian measurements

by Mark Rudelson - CISS 2006 (40th Annual Conference on Information Sciences and Systems , 2006
"... Abstract — This paper proves best known guarantees for exact reconstruction of a sparse signal f from few non-adaptive universal linear measurements. We consider Fourier measurements (random sample of frequencies of f) and random Gaussian measurements. The method for reconstruction that has recently ..."
Abstract - Cited by 108 (7 self) - Add to MetaCart
Abstract — This paper proves best known guarantees for exact reconstruction of a sparse signal f from few non-adaptive universal linear measurements. We consider Fourier measurements (random sample of frequencies of f) and random Gaussian measurements. The method for reconstruction that has

Dual Augmented Lagrangian Method for Efficient Sparse Reconstruction

by Ryota Tomioka, Masashi Sugiyama - IEEE Trans. Signal Process , 2009
"... We propose an efficient algorithm for sparse signal reconstruction problems. The proposed algorithm is an augmented Lagrangian method based on the dual sparse reconstruction problem. It is efficient when the number of unknown variables is much larger than the number of observations because of the du ..."
Abstract - Cited by 18 (3 self) - Add to MetaCart
We propose an efficient algorithm for sparse signal reconstruction problems. The proposed algorithm is an augmented Lagrangian method based on the dual sparse reconstruction problem. It is efficient when the number of unknown variables is much larger than the number of observations because

Analysisbased sparse reconstruction with synthesis-based solvers

by Nicolae Cleju, Maria G. Jafari, Mark D. Plumbley - in Proc. ICASSP 2012, 2012
"... Analysis based reconstruction has recently been introduced as an alternative to the well-known synthesis sparsity model used in a variety of signal processing areas. In this paper we convert the analysis exact-sparse reconstruction problem to an equivalent synthesis recovery problem with a set of ad ..."
Abstract - Cited by 3 (1 self) - Add to MetaCart
Analysis based reconstruction has recently been introduced as an alternative to the well-known synthesis sparsity model used in a variety of signal processing areas. In this paper we convert the analysis exact-sparse reconstruction problem to an equivalent synthesis recovery problem with a set
Results 1 - 10 of 2,871